Systems and methods for artificial intelligence (AI) theft prevention and recovery

ABSTRACT

Systems, apparatus, methods, and articles of manufacture for Artificial Intelligence (AI) theft prevention and recovery, such as by utilizing image object analysis to identify and report pilfering-type cargo or materials theft events.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

Theft of cargo, payloads, and other supplies or materials causesbillions of dollars of losses annually. While such theft may be ofvarious sorts and occur in various locations, one aspect that mostthefts have in common is that the chances of recovery of stolen goodsdrops dramatically as time passes. It is common, for example, for thechance of recovery to drop by approximately forty-five percent (45%) inthe first twenty-four hours (24 hrs) from the occurrence of the theftevent. Any delay in realizing or reporting a theft event may accordinglyhave significant negative impacts on the chances of recovery. Existingsystems provide advantages in cargo and material documentation andtracking, each of which may benefit recovery efforts. Such systems donot however, provide solutions for theft prevention or identification oftheft events.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures depict embodiments for purposes of illustration only. Oneskilled in the art will readily recognize from the following descriptionthat alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles describedherein, wherein:

FIG. 1 is a block diagram of a system according to some embodiments;

FIG. 2A and FIG. 2B are perspective diagrams of a system at differentpoints in time, according to some embodiments;

FIG. 3 is a diagram of an example augmented reality interface systemaccording to some embodiments;

FIG. 4 is a flow diagram of a method according to some embodiments;

FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, and FIG. 5E are diagrams of anexample interface system according to some embodiments;

FIG. 6 is a block diagram of an apparatus according to some embodiments;and

FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, and FIG. 7E are perspective diagramsof exemplary data storage devices according to some embodiments.

DETAILED DESCRIPTION I. Introduction

Existing systems for cargo and material documentation and tracking offersome advantages in assisting recovery efforts for stolen items but, failto prevent theft occurrences or to enhance theft (and/or damage or otherloss) event detection. Documentation applications allow for better cargomanagement practices that may facilitate record keeping, which may aidin recovery efforts by more readily identifying recovered items.Tracking is a well-known (but often expensive) means of locating lostproperty in response to a theft event. Neither of these options,however, facilitates theft prevention or identification.

In accordance with embodiments herein, these and other deficiencies ofexisting systems are remedied by providing systems, apparatus, methods,and articles of manufacture for Artificial Intelligence (AI) theftprevention and recovery utilizing image object analysis to identifytheft (and/or damage or other loss) events and provide AI-basedtheft/loss prevention guidance. In some embodiments, for example, images(or other sensor data) of cargo, payloads, materials, contents, and/orother objects may be captured at different points in time andautomatically compared and analyzed by stored AI logic and/or routinesto identify theft, damage, and/or other loss events. According to someembodiments, AI logic may be utilized to analyze theft and/or loss eventdata to provide predictive guidance for current and/or future cargo,payload, material, contents, and/or other object transportation and/orstorage.

II. AI Theft Prevention and Recovery Systems

Referring first to FIG. 1, a block diagram of a system 100 according tosome embodiments is shown. In some embodiments, the system 100 maycomprise a plurality of user devices 102 a-b in communication via orwith a network 104. According to some embodiments, one or more of theuser devices 102 a-b (such as a second user device 102 b, as depicted)may be disposed in, in communication with, and/or otherwise associatedwith a vehicle 106 (or other area, location, building, object, etc.). Insome embodiments, the system 100 may comprise and/or the user devices102 a-b may be in communication with a third-party device 108 and/or aserver 110. In some embodiments, the vehicle 106 may comprise a sensor116 (e.g., oriented and/or disposed to capture images, video, audio,and/or other readings descriptive of contents 130 of the vehicle 106).According to some embodiments, the sensor 116 may be coupled to and/orcomprise a portion of the contents 130.

In some embodiments, any or all of the devices 102 a-b, 106, 108, 110,116 may comprise and/or be in communication with a data storage and/ormemory device 140 a-c. A first user device 102 a may comprise a mobileelectronic device, such as a laptop or tablet computer housing a firstlocal or first memory device 140 a, for example, and/or the second userdevice 102 b may comprise a mobile electronic device, such as asmartphone housing a second local or second memory device 140 b.According to some embodiments, the server 110 may comprise (and/or be incommunication with) a third memory device 140 c. As depicted in FIG. 1,any or all of the devices 102 a-b, 106, 108, 110, 116, 140 a-c (or anycombinations thereof) may be in communication via the network 104. Insome embodiments, communications between and/or within the devices 102a-b, 106, 108, 110, 116, 140 a-c of the system 100 may be utilized tocapture and analyze images and/or other readings or input from thesensor 116 with respect to (and/or descriptive of) the contents 130 ofthe vehicle 106. Either user device 102 a-b may output an indication ofa theft or loss event and/or theft or loss prevention guidance, based oninput received from the sensor 116, for example, by execution of anapplication (or “app”) 142 a-b stored in the first memory device 140 aor the second memory device 140 b, respectively. According to someembodiments, such indication may be calculated, looked up, derived,defined, computed, and/or otherwise determined by analysis of imagery orother data captured by the sensor 116 (and/or by the user devices 102a-b and/or the vehicle 106) pursuant to an execution of AI code definedby the server 110 and/or by the app 142 a-b stored in the respectivememory device 140 a-b of the user devices 102 a-b.

Fewer or more components 102 a-b, 106, 108, 110, 116, 140 a-c, 142 a-band/or various configurations of the depicted components 102 a-b, 106,108, 110, 116, 140 a-c, 142 a-b may be included in the system 100without deviating from the scope of embodiments described herein. Insome embodiments, the components 102 a-b, 106, 108, 110, 116, 140 a-c,142 a-b may be similar in configuration and/or functionality tosimilarly named and/or numbered components as described herein. In someembodiments, the system 100 (and/or portion thereof) may comprise anAI-based theft prevention and recovery system and/or platform programmedand/or otherwise configured to execute, conduct, and/or facilitate themethod 400 of FIG. 4 herein, and/or portions thereof.

The user devices 102 a-b, in some embodiments, may comprise any type orconfiguration of computing, mobile electronic, network, user, and/orcommunication devices that are or become known or practicable. The userdevices 102 a-b may, for example, comprise one or more Personal Computer(PC) devices, computer workstations, tablet computers, such as an iPad®manufactured by Apple®, Inc. of Cupertino, Calif., and/or cellularand/or wireless telephones, such as an iPhone® (also manufactured byApple®, Inc.) or an LG V50 THINQ™ 5G smart phone manufactured by LG®Electronics, Inc. of San Diego, Calif., and running the Android®operating system from Google®, Inc. of Mountain View, Calif. In someembodiments, the user devices 102 a-b may comprise one or more devicesowned and/or operated by one or more users (not shown), such as a driverof the vehicle 106 (e.g., human and/or AI) and/or an electronic product(e.g., underwriting product) customer (or potential customer). The firstuser device 102 a may be operated by a cargo/payload loading entity thatloads the contents 130 in/on the vehicle 106, for example, and/or thesecond user device 102 b may be operated by a driver of the vehicle 106.According to some embodiments, the user devices 102 a-b may communicatewith the server 110 either directly or via the network 104 to (i)provide input from the sensor 116, (ii) provide information descriptiveof the contents 130 and/or the vehicle 106, (iii) to retrieve/receive AIanalysis results, (iv) to retrieve/receive theft/loss alerts and/ornotifications, (v) to retrieve/receive theft/loss preventionsuggestions/guidance, and/or (vi) to trigger and/or authorize one ormore third-party alerts by automatically contacting a police authorityand/or insurance carrier, in accordance with AI theft prevention andrecovery processes as described herein.

The network 104 may, according to some embodiments, comprise a LocalArea Network (LAN; wireless and/or wired), cellular telephone,Bluetooth®, Near Field Communication (NFC), and/or Radio Frequency (RF)network with communication links between the server 110, the userdevices 102 a-b, the third-party device 108, and/or the memory devices140 a-c. In some embodiments, the network 104 may comprise directcommunication links between any or all of the components 102 a-b, 106,108, 110, 116, 140 a-c of the system 100. The user devices 102 a-b may,for example, be directly interfaced or connected to one or more of theserver 110 and/or the third-party device 108 via one or more wires,cables, wireless links, and/or other network components, such networkcomponents (e.g., communication links) comprising portions of thenetwork 104. In some embodiments, the network 104 may comprise one ormany other links or network components other than those depicted inFIG. 1. The server 110 may, for example, be connected to the vehicle 106via various cell towers, routers, repeaters, ports, switches, and/orother network components that comprise the Internet and/or a cellulartelephone (and/or Public Switched Telephone Network (PSTN)) network, andwhich comprise portions of the network 104.

While the network 104 is depicted in FIG. 1 as a single object, thenetwork 104 may comprise any number, type, and/or configuration ofnetworks that is or becomes known or practicable. According to someembodiments, the network 104 may comprise a conglomeration of differentsub-networks and/or network components interconnected, directly orindirectly, by the components 102 a-b, 106, 108, 110, 116, 140 a-c ofthe system 100. The network 104 may comprise one or more cellulartelephone networks with communication links between the second userdevice 102 b and the server 110, for example, and/or may comprise an NFCor other short-range wireless communication path, with communicationlinks between the sensor 116, the first user device 102 a, and/or one ormore of the memory devices 140 a-c, for example.

According to some embodiments, the vehicle 106 may comprise any type,quantity, and/or configuration of transportation or other object or areathat is or becomes known or practicable. The vehicle 106 may comprise apassenger vehicle or fleet vehicle (e.g., a car, truck, train, boat,ship, and/or airplane), for example, that is utilized to transport ormove the contents 130 from a first location (e.g., an origin) to asecond location (e.g., a destination). In some embodiments, the vehicle106 may comprise a flatbed, lowboy, box, refrigerator (or “reefer”),container, tanker, dump, dry bulk, and/or car hauler semi-trailer,and/or associated tractor unit, and the contents 130 may comprise apayload or cargo thereof. According to some embodiments, the vehicle 106may instead comprise a location, such as a jobsite, construction site,and/or storage unit or site, and the contents 130 may comprise materialsand/or supplies, such as, but not limited to, construction materialsand/or equipment, tools, and/or building supplies and/or materials.

The third-party device 108, in some embodiments, may comprise any typeor configuration of a computerized processing device, such as a PC,laptop computer, computer server, database system, and/or otherelectronic device, devices, or any combination thereof. In someembodiments, the third-party device 108 may be owned and/or operated bya third-party (i.e., an entity different than any entity owning and/oroperating either the user devices 102 a-b or the server 110; such as acertificate, authentication, or cryptographic service provider, a policeor other law enforcement agency system, and/or an underwriting productcompany). The third-party device 108 may, for example, execute one ormore web services that provide for centralized firmware and/or logicupdating functionality, online AI object and/or image analysis, onlineAI theft/loss prevention analysis, and/or online navigational routingfunctionality. In some embodiments, the third-party device 108 mayprovide and/or transmit data (e.g., AI analysis results and/or lawenforcement data) to the second user device 102 b and/or the server 110.According to some embodiments, the third-party device 108 may comprise aplurality of devices and/or may be associated with a plurality ofthird-party entities (not shown).

In some embodiments, the server 110 may comprise an electronic and/orcomputerized controller device, such as a computer servercommunicatively coupled to interface with the user devices 102 a-band/or the third-party device 108 a (directly and/or indirectly). Theserver 110 may, for example, comprise one or more PowerEdge™ R830 rackservers manufactured by Dell®, Inc. of Round Rock, Tex. which mayinclude one or more Twelve-Core Intel® Xeon® E5-4640 v4 electronicprocessing devices. In some embodiments, the server 110 may comprise aplurality of processing devices specially programmed to execute and/orconduct processes that are not practicable without the aid of the server110. The server 110 may, for example, execute one or more coded rules tomanage wireless communications of the user devices 102 a-b (e.g., acellphone service provider) and/or the third-party device 108, and/ormay provide complex AI-based image/object analysis services, either ofwhich may not be capable of being conducted without the benefit of thespecially-programmed server 110. According to some embodiments, theserver 110 may be located remotely from one or more of the user devices102 a-b, the vehicle 106, and/or the third-party device 108. The server110 may also or alternatively comprise a plurality of electronicprocessing devices located at one or more various sites and/orlocations.

According to some embodiments, the server 110 may store and/or executespecially programmed instructions (e.g., stored in the third memorydevice 140 c, but not separately depicted in FIG. 1) to operate inaccordance with embodiments described herein. The server 110 may, forexample, execute one or more programs, modules, and/or routines (e.g.,the app 142 a-b) that facilitate the analysis, monitoring, and/orsafeguarding of the contents 130 of the vehicle 106, as describedherein. According to some embodiments, the server 110 may comprise acomputerized processing device, such as a centralized server utilized,for example, to (i) identify correlations between portions of receivedimages and/or sensor readings descriptive of the contents 130 and itemsthat comprise the contents 130, (ii) identify differences betweenimages/sensor readings descriptive of the contents 130 over time, (iii)classify the differences, (iv) trigger an alert and/or notificationbased on the classification, (v) classify a theft/loss risk based oninformation descriptive of the contents 130, and/or (vi) trigger and/orprovide theft/loss prevention guidance based on the classification ofthe contents 130, as described herein.

The sensor 116, in some embodiments, may comprise any type, quantity,and/or configuration of data gathering and/or input device that is orbecomes known or practicable. According to some embodiments, the sensor116 may comprise one or more sensors configured and/or coupled to sense,measure, calculate, and/or otherwise process or determine datadescriptive of the contents 130, the vehicle 106, and/or an environment(not separately depicted) in which they are disposed, such as lightmeasurements, strain measurements, temperature readings, moisture and/orhumidity readings, sound readings, vibration readings, weight readings,Infrared Radiation (IR) and/or microwave readings (e.g., motion sensorreadings and/or IR intensity readings), and/or location readings. Insome embodiments, the sensor 116 may be disposed and/or positioned toacquire data descriptive of the contents 130, the vehicle 106, and/orthe environment(s) thereof, but may be part of a separate device and/orobject, such as the vehicle 106 and/or the user devices 102 a-b. Thesensor 116 may comprise, for example, a camera and/or other sensor ofone or more of the user devices 102 a-b. In some embodiments, sensordata may be provided to the apps 142 a-b and/or the server 110 toidentify changes in the contents 130, e.g., to predict, prevent, and/oridentify theft and/or loss events thereof.

In some embodiments, the user devices 102 a-b, the third-party device108, the sensor 116, and/or the server 110 may be in communication withand/or comprise the memory devices 140 a-c. The memory devices 140 a-cmay comprise, for example, various databases and/or data storage mediumsthat may store, for example, image (and/or other sensor) data, objectidentification rules, object data, cargo/material rule and/or scenariodata, location data, theft/loss prevention rules and/or data,cryptographic keys and/or data, login and/or identity credentials,and/or instructions (e.g., AI-based theft/loss prevention and/orrecovery instructions and/or guidance) that cause various devices (e.g.,the third-party device 108 and/or the user devices 102 a-b) to operatein accordance with embodiments described herein.

The memory devices 140 a-c may store, for example, the apps 142 a-b,each of which may, when executed, participate in and/or cause AI-basedtheft/loss prevention and/or recovery, as described herein. In someembodiments, the memory devices 140 a-c may comprise any type,configuration, and/or quantity of data storage devices that are orbecome known or practicable. The memory devices 140 a-c may, forexample, comprise an array of optical and/or solid-state hard drivesconfigured to store digital image and/or video data, image and/or objectanalysis data and/or cargo/material analysis data (e.g., analysisformulas and/or mathematical models), credentialing instructions and/orkeys, and/or various operating instructions, drivers, etc. While thememory devices 140 a-c are depicted as stand-alone components of theuser devices 102 a-b and the server 110, the memory devices 140 a-c maycomprise multiple components. In some embodiments, a multi-componentmemory device 140 a-c may be distributed across various devices and/ormay comprise remotely dispersed components. Any or all of the userdevices 102 a-b, the vehicle 106, the contents 130, the third-partydevice 108, and/or the server 110 may comprise the memory devices 140a-c or a portion thereof, for example.

Referring now to FIG. 2A and FIG. 2B, perspective diagrams of a system200 at different points in time according to some embodiments are shown.The system 200 may comprise, for example, a user device 202 and avehicle 206, with the user device 202 being in communication with aremotely positioned server 210. According to some embodiments, the userdevice 202 may be disposed near or in (e.g., coupled to) the vehicle 206and/or may comprise a camera 216 a (and/or other sensor) and/or thevehicle 206 may comprise a sensor 216 b. According to some embodiments,the user device 202 may comprise and/or output an interface 220 that,e.g., displays information related to data captured by the camera 216 aand/or the sensor 216 b. In some embodiments (such as depicted), theuser device 202 (and/or the camera 216 a thereof) and/or the sensor 216b may be disposed and/or oriented to capture data descriptive of cargo230 a-c being transported by (or loaded or stored within) the vehicle206. As depicted for non-limiting purposes of example in FIG. 2A andFIG. 2B, the cargo 230 a-c may comprise a first cargo pallet 230 a, asecond cargo pallet 230 b, and/or a third cargo pallet 230 c loadedwithin the vehicle 206. The cargo pallets 230 a-c may comprise anynumber, type, and/or configuration of materials, payload, cargo,contents, supplies, tools, and/or other objects that are or become knownor practicable. In some embodiments, the cargo pallets 230 a-c maycomprise individual items, such as boxes, crates, and/or other objects.As depicted in FIG. 2A, for example, the first cargo pallet 230 a maycomprise a first box 230 a-1 and a second box 230 a-2 and/or the thirdcargo pallet 230 c may comprise a first crate 230 c-1 and a second crate230 c-2.

According to some embodiments, the user device 202 and/or the server 210may process data from the camera 216 a and/or from the sensor 216 b andexecute specially programmed code (software and/or firmware code) toidentify the boxes 230 a-1, 230 a-2, the crates 230 c-1, 230 c-2, and/orother objects, characteristics, and/or items descriptive of the cargo230 a-c, such as labels 232 a-1, 232 a-2, 232 c, pallet straps or seals234, box or crate straps or seals 236, and/or vehicle indicia 238 (e.g.,a license plate, VIN, trailer number, freight carrier logo, etc.). Theserver 210 may utilize the detection of the objects 230 a-1, 230 a-2,230 c-1, 230 c-2, 230 a-c, 232 a-1, 232 a-2, 232 c, 234, 236, 238 invarious manners to derive information and/or compute classificationsand/or conclusions regarding the cargo 230 a-c and/or the vehicle 206.In some embodiments, for example, the server 210 and/or the user device202 may identify a first box label 232 a-1 of the first box 230 a-1(e.g., based on input received from the camera 216 a and/or the sensor216 b) and execute an Optical Character Recognition (OCR) algorithm toidentify alphanumeric data depicted on the first box label 232 a-1. Inthe example case depicted in FIG. 2A and FIG. 2B, the server 210 and/orthe user device 202 may identify that the vehicle indicia 238 comprisesa license plate and that a partial plate number reads “11M”. Accordingto some embodiments, any data descriptive of any of the objects 230 a-1,230 a-2, 230 c-1, 230 c-2, 230 a-c, 232 a-1, 232 a-2, 232 c, 234, 236,238, such as recognized indicia thereof, location, size, shape,quantity, color, etc., may be stored in a database or memory device 240.The server 210 (and/or the user device 202) may comprise or be incommunication with the memory device 240, for example, and may storedata descriptive of the cargo 230 a-c and/or the vehicle 206 therein. Insome embodiments, the memory device 240 may store AI code 242 that isspecially programmed to (i) predict risks to the cargo 230 a-c based onthe data descriptive of the objects 230 a-1, 230 a-2, 230 c-1, 230 c-2,230 a-c, 232 a-1, 232 a-2, 232 c, 234, 236, 238 and/or (ii) identifyindications of theft or loss based on the data descriptive of theobjects 230 a-1, 230 a-2, 230 c-1, 230 c-2, 230 a-c, 232 a-1, 232 a-2,232 c, 234, 236, 238.

The AI code 242 may be utilized (e.g., executed) by the server 210and/or the use device 202, for example, to identify a type of cargo 230a-c (e.g., from labels 232 a-1, 232 a-2, 232 c) and identify orcalculate, for the specific type of cargo 230 a-c one or more risks. Inthe case that the cargo 230 a-c comprises home theatre electroniccomponents, for example, historic loss data stored in the memory device240 may be queried and/or analyzed to determine that such cargo 230 a-cfrequently suffers damage from high humidity levels. According to someembodiments, the interface 220 may be utilized to output guidance to auser/driver (not shown) that is descriptive of the sensitivity tohumidity levels. Similarly, the sensor 216 b may be utilized to detector measure humidity levels that are then compared, by the AI code 242 toa stored threshold to determine whether the current conditions of thecargo 230 a-c are within acceptable levels (e.g., to minimize thechances of damage). In some embodiments, the type of cargo 230 a-c maybe correlated to historic theft occurrences to identify characteristicsthat have been realized with some frequency in previous theft scenarios.The AI code 242 may determine, for example, that home theatre equipmenttypes of cargo 230 a-c are most frequently stolen after dark and/or whenit is not raining. In such embodiments, the interface 220 may beutilized to alert or notify the user/driver regarding impending orcurrent weather and/or time-of-day conditions that increase theft riskto the cargo 230 a-c. In some embodiments, potential discrepancies inthe cargo 230 a-c may be identified by object recognition executed bythe AI code 242. The AI code 242 may, for example, identify that thefirst box label 232 a-1 does not match a second box label 232 a-2 and/orthat a logo or label 232 c of the second crate 230 c-2 does not appearon the first crate 230 c-1. Either of these identifications may beevidence of improper loading or packing, mixed-goods cargo 230 a-c,and/or other characteristics of the cargo 230 a-c.

In some embodiments, the server 210 and/or the user device 202 mayexecute the AI code 242 that analyzes the cargo 230 a-c over time toderive data descriptive of possible theft, damage, and/or other lossevents in relation to the cargo 230 a-c and/or the vehicle 206. Asdepicted in FIG. 2A, for example, the camera 216 a of the user device202 may capture an image (or other data) descriptive of the cargo 230a-c (or a portion therof, such as the third cargo pallet 230 c) at afirst point in time and may identify various attributes and/orcharacteristics of the first instance of the cargo 230 a-c, such as adimension/height, shape, location (absolute and/or relative to otherobjects), etc. According to some embodiments, the user device 202 maytransmit the image to the server 210 and the server may execute the AIcode 242 to identify objects in the image, such as the first cargopallet 230 a, the third cargo pallet 230 c, and/or the pallet straps 234of the third cargo pallet 230 c. In some embodiments, such as in thecase that the first cargo pallet 230 a, the third cargo pallet 230 c,and/or the pallet straps 234 of the third cargo pallet 230 c are foundto be present in multiple images and/or video frames captured by thecamera 216 a, the server 210 may identify a second instance or view ofthe cargo 230 a-c at a second time, as depicted in FIG. 2B. According tosome embodiments, execution of the AI code 242 may cause a comparison ofany initial or first attributes of the first instance of the cargo 230a-c to an identified second attribute of the second instance of thecargo 230 a-c to derive, compute, and/or calculate a characteristic ofthe cargo 230 a-c. In the example depicted in FIG. 2A and FIG. 2B, forexample, the AI code 242 may determine (e.g., via an object-basedanalysis of data descriptive of the cargo 230 a-c captured at differenttimes) one or more instances of missing objects 260 a-b, damaged objects262, damaged pallet straps 264, and/or damaged crate seals 266.

According to some embodiments, the comparison of the attributes may beutilized to lookup, calculate, and/or otherwise compute quantitativemetrics regarding the cargo 230 a-c. Based on the magnitude of thedifferences in dimensions/measurements, textures, heat signatures,colors, and/or locations, for example, the AI code 242 may (i) identifyan instance of a missing pallet 260 a, (ii) identity an instance of amissing crate 260 b, (iii) identify an instance of a damaged crate 262,(iv) identify an instance of a compromised pallet strap 264, and/or (v)identify an instance of a compromised crate seal 266. In someembodiments, the presence or absence of the sensor 216 b may also oralternatively be taken into account when analyzing a status orclassification of the cargo 230 a-c. In the case that the identifiedmissing crate 260 b was a crate to which the sensor 216 b was coupled,for example, the instance of the missing crate 260 b may be identifiedby object detection and analysis and/or by analyzing signals from thesensor 216 b. In the case that no signal from the sensor 216 b can bedetected, for example, it may be presumed that the sensor 216 b andassociated crate are missing from the third cargo pallet 230 c. In someembodiments, such as in the case that the sensor 216 b comprises alocation tracking device, such as an RFID and/or GPS device, datareceived from the sensor 216 b may indicate that the sensor 216 b andassociated crate are not located proximate to the vehicle 206, e.g., anindication of theft. According to some embodiments, the interface 220may output an indication of the missing and/or damaged objects 260 a-b,262, 264, 266 and/or may provide a mechanism for the user to initiate anautomatic reporting of the missing and/or damaged objects 260 a-b, 262,264, 266 (e.g., to a law enforcement agency, underwriting agency,shipping company, etc.).

III. AI Theft Prevention and Recovery Augmented Reality InterfaceSystems

Turning now to FIG. 3, a diagram of an example augmented realityinterface system 300 according to some embodiments is shown. In someembodiments, the augmented reality interface system 300 may comprise amobile electronic user device 302 that outputs one or more interfaces320. According to some embodiments, the interface 320 may comprise oneor more of a web page, web form, database entry form, API, spreadsheet,table, and/or application or other GUI via which a user or other entitymay receive and/or enter data (e.g., provide or define input) inassociation with AI theft prevention and recovery, as described herein.The interface 320 may, for example, comprise a front-end of an AItheft/loss prevention and/or recovery program and/or platform programmedand/or otherwise configured to execute, conduct, and/or facilitate themethod 400 of FIG. 4 herein, and/or portions thereof. In someembodiments, the interface 320 may be output via one or morecomputerized and/or specially-programmed computers (e.g., the userdevices 102 a-b, 202, 302, 502, the third-party device 108, the servers110, 210, 610 all of FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 5A, FIG. 5B,FIG. 5C, FIG. 5D, FIG. 5E, and/or FIG. 6 herein), computer terminals,computer servers, computer systems and/or networks, and/or anycombinations thereof (e.g., by one or more multi-threaded and/ormulti-core processing units of an online and/or client-server AI objectanalysis processing system.

According to some embodiments, the interface 320 may comprise one ormore tabs and/or other segmented and/or logically-presented data forms,areas, and/or fields. In some embodiments, the interface 320 may beconfigured and/or organized to allow and/or facilitate management ofcargo, supplies, payloads, materials, and/or other objects byfacilitating theft and/or damage event identification. According to someembodiments, the interface 320 may comprise an output of a mobile deviceapplication that facilitates theft/loss identification by utilizingaugmented reality overlays on images/video feed descriptive of cargo orother objects of interest. As depicted in FIG. 3, for example, a firstversion (or page or instance) of the interface 320 may comprise a “CargoCheck” interface (e.g., defining a first input and/or output mechanism)that provides and/or includes an augmented reality display 320-1, amissing pallet indicator 320-2, a missing crate indicator 320-3, adamaged crate indicator 320-4, a report theft button 320-5, and/or areport damage button 320-6 (e.g., each of which may comprise one or moredata entry mechanisms, tools, objects, and/or features). While theinterface 320 of FIG. 3 depicts output that is provided as atwo-dimensional (2D) representation of, e.g., a rear view of a traileror storage container, the interface 320 may also or alternatively beprovided as a three-dimensional (3D), perspective, top-down or overhead,cross sectional, and/or other view or representation that is or becomesknown or practicable. According to some embodiments, the interface 320may comprise different layers of data for the augmented reality display320-1, such that a user may scroll or browse through different views tobetter inspect all items of a cargo or material pile/area, e.g., suchthat items hidden from view by other items may be readily inspected.

In some embodiments, the first version (or page or instance) of theinterface 320 may be utilized by a user (such as a truck driver, cargoloader/unloader, cargo carrier entity, customs official) to readilyidentify whether one or more objects have been subject to theft, damage,or other loss. An image, video, and/or other sensor-reading display ofan object, such as cargo 330 a-c (e.g., the cargo 230 a-c of FIG. 2A andFIG. 2B), may, for example, be overlaid and/or represented by theaugmented reality display 320-1. The augmented reality display 320-1 maycomprise, in some embodiments, a virtual representation of the objectsidentified in the cargo 330 a-c and/or may comprise virtualrepresentations and/or graphics overlaid upon a previously capturedimage/video/etc. of the cargo 330 a-c. According to some embodiments,the augmented reality display 320-1 may comprise graphical itemsoverlaid on a live feed from a camera (not shown) of the user device302. In some embodiments, the live feed or captured image(s) may becompared to previous image/sensor data descriptive of the cargo 330 a-cby utilizing AI object detection algorithms as described herein.

According to some embodiments, the augmented reality display 320-1 maygenerate and display the missing pallet indicator 320-2 on a portion ofthe augmented reality display 320-1 that is computed to coincide with aprevious location of a first item of cargo 330 a (e.g., the first cargopallet 230 a of FIG. 2A). In some embodiments, the missing palletindicator 320-2 may comprise a virtual representation of the size,shape, color, location, and/or other characteristic and/or attribute ofa missing cargo pallet comprising the first cargo item 330 a. Asdepicted, the missing pallet indicator 320-2 may comprise a highlighted,animated (e.g., flashing), and/or otherwise distinctive graphical object(e.g., annotated with the descriptor “missing”) overlaid on theaugmented reality display 320-1 in a position that represents where thefirst cargo item 330 a should be (e.g., was previously located).According to some embodiments, the missing pallet indicator 320-2 maycomprise an interactive input/output element. In the case that themissing pallet indicator 320-2 is activated upon a triggering and/orreceipt of input from the user (e.g., a properly positioned click of amouse or other pointer with respect to the missing pallet indicator320-2), for example, data descriptive of the missing cargo pallet 330 amay be provided (e.g., size, weight, ID numbers, content descriptions,value, etc.).

In some embodiments, the augmented reality display 320-1 may generateand display the missing crate indicator 320-3 on a portion of theaugmented reality display 320-1 that is computed to coincide with aprevious location of a portion of a third item of cargo 330 c (e.g., aportion of the third cargo pallet 230 c of FIG. 2A and/or FIG. 2B). Insome embodiments, the missing crate indicator 320-3 may comprise avirtual representation of the size, shape, color, location, and/or othercharacteristic and/or attribute of a missing crate of the third item ofcargo 330 c. As depicted, the missing crate indicator 320-3 may comprisea highlighted, animated (e.g., flashing), and/or otherwise distinctivegraphical object (e.g., annotated with the descriptor “missing”)overlaid on the augmented reality display 320-1 in a position thatrepresents where the missing crate from the third item of cargo 330 cshould be (e.g., was previously located). According to some embodiments,the missing crate indicator 320-3 may comprise an interactiveinput/output element. In the case that the missing crate indicator 320-3is activated upon a triggering and/or receipt of input from the user(e.g., a properly positioned click of a mouse or other pointer withrespect to the missing crate indicator 320-3), for example, datadescriptive of the missing crate from the third item of cargo 330 c maybe provided (e.g., size, weight, ID numbers, content descriptions,value, etc.).

According to some embodiments, areas of damage or other indications ofloss that are identified via AI logic object-based analysis may bedepicted via the augmented reality display 320-1 by generation of thedamaged crate indicator 320-4. The damaged crate indicator 320-4 maycomprise, for example, a virtual representation of the size, shape,color, location, and/or other characteristic and/or attribute of adamaged portion of a crate of the third item of cargo 330 c. Asdepicted, the damaged crate indicator 320-4 may comprise a highlighted,animated (e.g., flashing), and/or otherwise distinctive graphical object(e.g., annotated with the descriptor “damaged”) overlaid on theaugmented reality display 320-1 in a position that represents where thedamage to the crate from the third item of cargo 330 c is located (e.g.,where it has been identified by the AI analysis). According to someembodiments, the damaged crate indicator 320-4 may comprise aninteractive input/output element. In the case that the damaged crateindicator 320-4 is activated upon a triggering and/or receipt of inputfrom the user (e.g., a properly positioned click of a mouse or otherpointer with respect to the damaged crate indicator 320-4), for example,data descriptive of the identified damage and/or descriptive of thecrate from the third item of cargo 330 c may be provided (e.g.,estimated type of damage, size, weight, ID numbers, contentdescriptions, value of crate contents, estimated damaged value (e.g.,loss amount), etc.).

In some embodiments, the first version (or page or instance) of theinterface 320 may comprise either or both of the report theft button320-5 and the report damage button 320-6. The report theft button 320-5may be utilized, for example, to report a suspected instance of theft(as indicated by the missing pallet indicator 320-2 and/or the missingcrate indicator 320-3) to various entities, such as law enforcement, aninsurance carrier, a shipping company, customer, supplier, etc.Selection of the report theft button 320-5 may, in some embodiments,trigger an automatic transmission to such entities (e.g., from the userdevice 302 and/or a centralized server device—not shown in FIG. 3).According to some embodiments, the transmission may comprise datadescriptive of the missing cargo 330 a-c and/or portions of missingcargo (e.g., crates, boxes), data descriptive of a vehicle (not shown)and/or location of the suspected theft, a timestamp, cryptographicinformation, cargo identifiers, cargo tracking information, etc.Similarly, the report damage button 320-6 may be utilized to report asuspected instance of damage or loss (as indicated by the damaged crateindicator 320-4) to various entities, such as an insurance carrier, ashipping company, customer, supplier, etc. Selection of the reportdamage button 320-6 may, in some embodiments, trigger an automatictransmission to such entities (e.g., from the user device 302 and/or acentralized server device—not shown in FIG. 3). According to someembodiments, the transmission may comprise data descriptive of thedamaged cargo 330 a-c and/or portions of damaged cargo (e.g., crates,boxes), data descriptive of characteristics and/or attributes of avehicle (not shown) and/or location of the damage discovery (e.g.,weather conditions, sensor readings), a timestamp, cryptographicinformation, cargo identifiers, cargo tracking information, etc.

According to some embodiments, the interface 320 may permit a user toselectively report and/or investigate specific identified instances ofsuspected theft and/or loss. In the case that the user believes thefirst cargo item 330 a is not missing, but has recently been unloaded,for example, the user may select the missing crate indicator 320-3 todesignate the missing crate from the third item of cargo 330 c forreporting, and then select the report theft button 320-5 toautomatically transmit details of the missing crate from the third itemof cargo 330 c to a pre-programmed entity address (e.g., a lawenforcement e-mail address, a security company fax number, a freightcompany's text reporting number, etc.). In some embodiments, multipleitems of cargo 330 a-c and/or portions thereof may be selectivelydesignated for reporting to different entities. In such a manner, forexample, instances of theft and/or loss may be quickly and easilyidentified and reported, and any or all relevant information that may beuseful for recovery efforts may be automatically provided to variousremote and/or relevant parties.

IV. AI Theft Prevention and Recovery Processes

Referring now to FIG. 4, a flow diagram of a method 400 according tosome embodiments is shown. In some embodiments, the method 400 may beperformed and/or implemented by and/or otherwise associated with one ormore specialized and/or specially-programmed computers (e.g., the userdevices 102 a-b, 202, 302, 502, the third-party device 108, the servers110, 210, 610 all of FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 5A, FIG. 5B,FIG. 5C, FIG. 5D, FIG. 5E, and/or FIG. 6 herein), computer terminals,computer servers, computer systems and/or networks, and/or anycombinations thereof. In some embodiments, the method 400 may beembodied in, facilitated by, and/or otherwise associated with variousinput mechanisms and/or interfaces (e.g., the interfaces 220, 520 a-e,620 of FIG. 2A, FIG. 2B, FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E,and/or FIG. 6 herein).

The process diagrams and flow diagrams described herein do notnecessarily imply a fixed order to any depicted actions, steps, and/orprocedures, and embodiments may generally be performed in any order thatis practicable unless otherwise and specifically noted. While the orderof actions, steps, and/or procedures described herein is generally notfixed, in some embodiments, actions, steps, and/or procedures may bespecifically performed in the order listed, depicted, and/or describedand/or may be performed in response to any previously listed, depicted,and/or described action, step, and/or procedure. Any of the processesand methods described herein may be performed and/or facilitated byhardware, software (including microcode), firmware, or any combinationthereof. For example, a storage medium (e.g., a hard disk, Random AccessMemory (RAM) device, cache memory device, Universal Serial Bus (USB)mass storage device, and/or Digital Video Disk (DVD); e.g., the memorydevices 140 a-c, 240, 640, 740 a-e of FIG. 1, FIG. 2A, FIG. 2B, FIG. 6,FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, and/or FIG. 7E herein) may storethereon instructions that when executed by a machine (such as acomputerized processor) result in performance according to any one ormore of the embodiments described herein.

In some embodiments, the method 400 may comprise receiving (e.g., via anelectronic network and/or by an electronic processing device) cargo data(e.g., at or before a first point in time), at 402. A mobile electronicdevice may be utilized, for example, to input and/or transmit a bill oflading, manifest, Stock Keeping Unit (SKU) list, origin, destination,freight company information, assigned or required vehicle or equipmentinformation, characteristic, quantity, and/or attribute data descriptiveof a cargo, payload, materials, contents, supplies, tools, and/or otherdesired grouping of objects. According to some embodiments, the cargodata may define various attributes, such as size, mass, weight,quantity, IR-signature, color, etc. In some embodiments, the cargo datamay comprise a correlation of one or more identifiers (such as SKUidentifiers, serial numbers, security tag or seal identifiers) with theattributes and/or characteristics of a unit of cargo/materials/etc.According to some embodiments, the cargo data may be input, acquired,transmitted (e.g., by a user device), and/or received (e.g., by acentralized server device) upon loading, purchase, and/or acquisition ofthe cargo. Freight loading personnel may, for example, utilize asmartphone executing a specially programmed application to documentdetails of the cargo loaded onto a particular vehicle once the vehicleis loaded.

According to some embodiments, the method 400 may comprise receiving(e.g., via the electronic network and/or by the electronic processingdevice) at least one first cargo “image” at a first point in time, at404. The “image” may comprise, for example, a still photo, videofootage, and/or a list, table, and/or matrix of data values, e.g.,captured, sensed, and/or recorded by a camera and/or other sensor. Insome embodiments, the image may be captured at a time after orconcurrently with the receiving of the cargo data at 402. According tosome embodiments, an AI device, such as a server and/or mobile userdevice, may comprise both of a Graphics Processing Unit (GPU) to permitreal-time analysis of captured data and AI processing logic and/oralgorithms for analyzing the captured data. According to someembodiments, images and/or other data captured or sensed by thecamera/sensor may be sent to the GPU, which may receive and process thedata (e.g., in accordance with stored (software and/or firmware)graphics processing algorithms or rules). In some embodiments, the GPUmay send the processed data/images to a Central Processing Unit (CPU) orother logical device that may, for example, receive and process theGPU-processed data/images, e.g., in accordance with stored AIinstructions. According to some embodiments, the image may be receivedby a processing device of a server from a mobile electronic device of auser, such as a freight loader, shipper, etc. In some embodiments, thefirst image(s) may be captured, acquired, sensed, and/or receivedautomatically by an affixed security camera and/or sensor coupled todetect data descriptive of the cargo/materials.

In some embodiments, the method 400 may comprise computing a correlation(e.g., via AI analysis performed by a CPU and/or processing device)between portions of the at least one image and cargo items, at 406. AIinstructions may comprise one or more object detection algorithms, forexample, that are coded to identify various types of objects in theimage data descriptive of the cargo, such as, but not limited to, boxes,crates, seals, straps, labels, tags, logos, and/or shapes. Objectdetection algorithms may generally store predefined shapes, colors,and/or patterns that the AI device (e.g., server and/or user device) mayutilize to search through captured images/data to identify occurrencesof such shapes, colors, and/or patterns, and thereby identify one ormore objects comprising the cargo. According to some embodiments,identified objects may be classified into different types, such asboxes, crates, etc. According to some embodiments, information may beacquired from various identified objects, such as product identifiers(from labels), model numbers (from labels and/or logos), serial numbers(from labels or RFID tags), and/or locations (e.g., based on GPScoordinates and/or estimated distances from other objects). In such amanner, for example, various portions of the image(s) may be tagged(e.g., in a memory device) as being associated with certain itemsidentified in the cargo data, such as certain cargo pallets, crates,boxes, shipments, etc.

According to some embodiments, the method 400 may comprise determining(e.g., by the CPU/processing device) whether more cargo should beanalyzed, at 408. In the case that no cargo objects/items have beenidentified and/or correlated (and/or additional objects/items should beidentified and/or correlated), the method 400 may proceed back toreceiving additional cargo images at 404 (and/or to receiving additionalcargo data at 402). In the case that at least one object/item has beenidentified and/or correlated (at 406) and/or no additional objects/itemsshould be identified, the method 400 may proceed to and/or comprisereceiving (e.g., via the electronic network and/or by the electronicprocessing device) at least one second cargo image at a second point intime, at 410. In some embodiments, the second image(s) may be receivedby a user device (e.g., as camera and/or sensor input) and/or by aserver (e.g., from a user device) at some time after a receiving of thefirst image(s) at 404. In the case that the first image(s) at 404 arereceived at a time of loading, delivery, and/or shipping origin, forexample, the second image(s) may be captured and/or received at awaypoint (e.g., truck stop/fuel station), destination, transfer point,weigh station, customer and/or job site, etc. According to someembodiments, the second image(s) may be acquired from a similarperspective and/or under similar guidelines as the first image(s), forexample, comprising digital photographs taken from a similar vantagepoint with respect to the cargo/materials. In some embodiments, guidancemay be defined and/or output to direct a user to acquire the secondimage(s) in a manner that is conducive to comparing the second image(s)to the first image(s). The user may be directed by directional guidanceprompts output via an interface of the user's mobile device, forexample, to position the mobile device at a certain location and/orangle to capture the second image(s) from the desired perspective. Insome embodiments, the second image(s) may be captured, acquired, sensed,and/or received automatically by an affixed security camera and/orsensor coupled to detect data descriptive of the cargo/materials.

In some embodiments, the method 400 may comprise identifying (e.g., bythe CPU/processing device and/or by a server device and/or a mobiledevice application) a portion of the images that differs between thepoints in time, at 412. The AI code may be utilized, for example, tocompare the first and second image(s) to identify differences therein.According to some embodiments, such a comparison may comprise matchingor aligning the images. Objects and/or features detected and/oridentified in the first and second images may, for example, be comparedto determine an alignment and/or correlation between the first andsecond image(s). In the case that the first and second image(s) are notrepresentative of the same angle or perspective of the cargo/materials,for example, objects in the images may be analyzed to achieve analignment of at least one portion of the first image(s) and at least oneportion of the second image(s). According to some embodiments, aftermatching/corresponding objects have been utilized to align the images,differences between the images may be identified. AI object recognitionalgorithms may be executed, for example, to identify or locate anyobjects previously identified in the first image(s) (e.g., andcorrelated to items of the cargo/materials). In some embodiments,AI-based object analysis may identify one or more objects in the imagesthat have (i) moved, (ii) changed appearance (and/or changed withrespect to another attribute, such as heat signature, RF reading),and/or (iii) failed to be located (e.g., in either the first image(s)for newly-identified objects or in the second image(s) fororiginally-identified objects). According to some embodiments, the typeof difference in the identified objects may be classified (e.g.,categorized), such as a moved object, a missing object, a new object,and/or a damaged (or presumed damaged) object.

According to some embodiments, the method 400 may comprise identifying(e.g., by the CPU/processing device and/or by a server device and/or amobile device application) a changed cargo item, at 414. Based on thecorrelation at 406, for example, any objects identified as changed (at412) within the images between the two points in time, may becross-referenced with known/identified items of cargo, e.g., asidentified by the cargo data received at 402. In some embodiments, theclassification of the change may be analyzed with respect to theparticular item of cargo to derive and/or compute a statusclassification. In the case of living cargo, such as a horse, forexample, since movement is to be expected, an identified movement of theitem may be classified as “acceptable” or as a non-event. In the case ofa fragile item packed in a shipping crate with a certain end intended toface upwards, however, a tipping, rotation, or other movement thatexceeds a threshold (e.g., linear and/or radial) may be classified asexcessive movement, potential damage, rough handling, etc. In someembodiments, a status classification may be calculated or computed foreach item of cargo that can be identified by the AI logic. In someembodiments, the AI logic may be specifically trained to identify normalor expected movements or changes such as shifting cargo items, movinglivestock, and/or changes in appearance due to different lighting,timing, or other conditions.

According to some embodiments, the method 400 may comprise determining(e.g., by the CPU/processing device) whether there are (or should be)more changes, at 416. In the case that no changes in or of objects/itemshave been identified (and/or additional changes and/or objects/itemsshould be identified), for example, the method 400 may proceed back tocapture or receive more second images/data at 410 (and/or identify morechanges/differences at 412). In some embodiments, the AI image/objectanalysis may be scheduled to automatically occur at various times. Insuch embodiments, the capturing and/or receiving of second images/dataat 410, the identifying of changes in the images at 412, and/or theidentifying of the changes in the cargo items at 414 may proceed and/orbe repeated automatically upon certain triggering events (e.g., vehiclestops, elapsed miles driven, detection of bumps, tipping, or hard stops,detection of an opened trailer/cargo door, detection of excessive noiselevels, etc.) and/or upon expiration of a predefined time period/window(e.g., every hour). In some embodiments, the AI analysis (at 410, 412,414) may be selectively initiated and/or triggered by user input. Thedriver of a tractor trailer may, for example, select a button on aninterface of a mobile electronic device to initiate an AI scan of apayload to check on the status of the freight items being hauled. In thecase of a self-driving and/or autonomous vehicle, the AI and/or otherdriving logic may automatically initiate the payload scan based onstored rules and/or logic. In some embodiments, a contractor mayselectively initiate a scan of a pile of lumber and/or other materialsupon arriving at a jobsite in the morning to check and make sure that notheft has occurred overnight.

In the case that no more input and/or analysis is available and/orneeded, the method 400 may proceed to output a changed cargo alert, at418. In some embodiments, the user's mobile device and/or an in-vehicledevice may be utilized to output status check results at various pointsin time (e.g., at the close of a time window). According to someembodiments, a cargo sensor may comprise a short-range communicationdevice operable to transmit an indication of the cargo status (e.g.,images, sensor data, calculations) to the vehicle and/or to a mobileelectronic device (e.g., a smart phone) utilized by a driver thereof. Insome embodiments, the result/status may be output via a webpage servedby a web server in communication with the sensor and, e.g., displayedvia a user's mobile device. According to some embodiments, a speciallyprogrammed mobile device application of a user's device may be coded toreceive and/or capture image data (e.g., from the sensor device and/orfrom an integrated sensor/camera), process the data, transmit the datato a server, and/or receive cargo status AI-processing results from theserver (e.g., an AI algorithm execution server) that operates inconjunction therewith. In some embodiments, the method 400 may comprisereeving user input and/or commands in response to the cargo status. Theuser may review possible instances of theft, damage, and/or other lossor concern, for example, and may provide input that triggers an alert,notification, and/or report to be transmitted to a third-party, such asa law enforcement agency, customs, border, or import agency, insurancecarrier, freight carrier, freight broker, shipper, supplier, customer,etc. According to some embodiments, alerts, notifications, and/orreports may be automatically sent based on stored rules and storedcontact/address information.

V. AI Theft Prevention and Recovery Interfaces

Turning now to FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, and FIG. 5E, diagramsof an example interface system 500 according to some embodiments areshown. In some embodiments, the interface system 500 may comprise amobile electronic user device 502 that outputs one or more interfaces520 a-e. According to some embodiments, the interfaces 520 a-e maycomprise one or more of a web page, web form, database entry form, API,spreadsheet, table, and/or application or other GUI via which a user orother entity may receive and/or enter data (e.g., provide or defineinput) in association with AI theft prevention and recovery, asdescribed herein. The interfaces 520 a-e may, for example, comprise afront-end of an AI theft prevention and recovery program and/or platformprogrammed and/or otherwise configured to execute, conduct, and/orfacilitate the method 400 of FIG. 4 herein, and/or portions thereof. Insome embodiments, the interfaces 520 a-e may be output via one or morecomputerized and/or specially-programmed computers (e.g., the userdevices 102 a-b, 202, 302, 502, the third-party device 108, the servers110, 210, 610 all of FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 5A, FIG. 5B,FIG. 5C, FIG. 5D, FIG. 5E, and/or FIG. 6 herein), computer terminals,computer servers, computer systems and/or networks, and/or anycombinations thereof (e.g., by one or more multi-threaded and/ormulti-core processing units of an online and/or client-server AI objectanalysis processing system).

According to some embodiments, the interfaces 520 a-e may comprise oneor more tabs and/or other segmented and/or logically-presented dataforms and/or fields. In some embodiments, the interfaces 520 a-e may beconfigured and/or organized to allow and/or facilitate viewing,retrieval, and/or selection of various object identification, theft/lossevent identification, cargo status, and/or theft/loss reporting data,e.g., for a particular location, driver, group of drivers, and/orvehicle or group of vehicles (e.g., a fleet). According to someembodiments, the interfaces 520 a-e may comprise a menu or “home” pagefrom which a user may select one or more options that initiate specificfunctionality of a mobile device application. As depicted in FIG. 5A,for example, a first version (or page or instance) of the interface 520a may comprise a “Cargo Loading” interface (e.g., defining a first inputand/or output mechanism) that provides and/or includes a cargo manifest520-1, a setup sensor button 520-2, a scan item button 520-3, an editdetails button 520-4, and/or “Push When Loading Complete” button 520-5(e.g., each of which may comprise one or more data entry mechanisms,tools, objects, and/or features).

In some embodiments, the first version (or page or instance) of theinterface 520 a may be utilized by a user, such as a cargo loader/riggerto document cargo loaded into/onto a vehicle. Each item loaded may bescanned, imaged, and/or entered into the manifest 520-1, for example, asit is loaded. In some embodiments, data defining the manifest 520-1and/or a portion thereof may be received (e.g., downloaded) from one ormore remote devices (not shown; such as the third-party device 108 ofFIG. 1 herein). According to some embodiments, the setup sensor button520-2 may be utilized to initialize and/or configure a sensor coupled toan item of cargo and/or to the vehicle (e.g., a trailer securitysensor). In some embodiments, the scan item button 520-3 may be utilizedto enter a new item or group of items into the manifest 520-1. The scanitem button 520-3 may, for example, cause a camera and/or other sensor(not shown) to capture images, video, audio, and/or sensor data, such astemperature, humidity, and/or light readings, etc., descriptive of oneor more items of cargo. In some embodiments, the scan item button 520-3may cause a barcode or other machine-readable indicia to beinterrogated, read, and/or decoded. According to some embodiments, thescan item button 520-3 may cause an AI object-based analysis of imagedata to identify loaded items and/or to correlate identified objects toitems already entered into the manifest 520-1. The edit details button520-4 may, for example, be utilized by the user/loader to enter and/ormodify details defining characteristics and/or attributes of cargoitems. In some embodiments, acceptable temperature, light, humidity,and/or vibration thresholds, safety data (e.g., flash points, hazardousmaterial parameters), and/or weights and dimensions may be entered intothe manifest 520-1 and/or stored in association with items identified bythe manifest 520-1. In some embodiments, the “Push When LoadingComplete” button 520-5 may, upon a triggering and/or receipt of inputfrom the user (e.g., a properly positioned click of a mouse or otherpointer with respect to the “Push When Loading Complete” button 520-5),call, initialize, and/or cause a generation of a second version (or pageor instance) of the interface 520 b.

As depicted in FIG. 5B, for example, the second version (or page orinstance) of the interface 520 b may comprise a “Cargo Pickup” interface(e.g., defining a second input and/or output mechanism) that providesand/or includes shipment details 520-6, a verify ID button 520-7, adownload manifest button 520-8, an inspect cargo button 520-9, a pairsensor button 520-10, and/or “Push When Pickup Complete” button 520-11(e.g., each of which may comprise one or more data entry mechanisms,tools, objects, and/or features). In some embodiments, the secondversion (or page or instance) of the interface 520 b may be utilized bya user, such as a freight hauler or driver, to document and/or verifytransfer of custody of the cargo from the loader to the driver. Thedriver may readily view the shipment details 520-6 to verify that thecorrect cargo is being picked up, for example, and/or may select theverify ID button 520-7 to authenticate the loader, rigging company,supplier, etc. The verify ID button 520-7 may permit the driver to viewa photo of authorized loading personnel, for example, and/or mayinitiate a cryptographic routine to authenticate a code or identifierprovided by the loader.

According to some embodiments, the loader may perform a similar functionon an alternate version of the second version of the interface 520 b(not shown) to verify the driver's credentials and/or identity. In someembodiments, the download manifest button 520-8 may be utilized toquery, pull, and/or acquire a copy of the manifest 520-1 from theloader. The user device 502 of FIG. 5B may be utilized by the driver,for example, to download a copy of the manifest 520-1 directly from theuser device 502 utilized by the loader in FIG. 5A. According to someembodiments, in the case that the manifest 520-1 is saved remotely,e.g., to a server (not shown), the user device 502 may access the serverto download the manifest 520-1. In some embodiments, the manifest 520-1may only be capable of being downloaded by the driver in the case thatthe driver's identify is verified by the loader or in the case that theloader otherwise enables the user device 502 to access the information.

In some embodiments, the inspect cargo button 520-9 may be utilized toinitiate an AI scan of the cargo and/or cargo images and/or sensor datato set a baseline for the characteristics and/or attributes of the cargoand/or to verify that the cargo characteristics and/or attributes matchthose as detailed in or stored in relation to the manifest 520-1.According to some embodiments, the inspect cargo button 520-9 mayinitiate an AI routine that is specially programmed to identify properor improper loading of goods. In some embodiments, the inspect cargobutton 520-9 may cause an interrogation of one or more sensors coupledto detect information descriptive of the cargo.

According to some embodiments, the pair sensor button 520-10 may beutilized to initiate communications between the user device 502 and asensor (not shown) associated with the cargo. One or more short-rangewireless cameras, RFID devices, GPS devices, light sensors, weightsensors, temperature sensors, humidity sensors, etc., may, for example,be connected to the user device 502 by sharing various electronichandshake data, identifiers, codes, cryptographic keys, etc. Accordingto some embodiments, the loader may authorize the pairing and/or providethe necessary keys and/or codes to the driver so that the driver mayreceive data from any utilized cargo sensors. In some embodiments, the“Push When Pickup Complete” button 520-11 may, upon a triggering and/orreceipt of input from the user (e.g., a properly positioned click of amouse or other pointer with respect to the “Push When Pickup Complete”button 520-11), call, initialize, and/or cause a generation of a thirdversion (or page or instance) of the interface 520 c.

As depicted in FIG. 5C, for example, the third version (or page orinstance) of the interface 520 c may comprise a “Routing” interface(e.g., defining a third input and/or output mechanism) that providesand/or includes a routing map 520-12, a suggested route button 520-13, aminimize theft button 520-14, a minimize damage button 520-15, and/or“Push When Ready to Begin Route” button 520-16 (e.g., each of which maycomprise one or more data entry mechanisms, tools, objects, and/orfeatures). In some embodiments, the third version (or page or instance)of the interface 520 c may be utilized by a user, such as a freighthauler or driver, to plan, select, and/or begin traversal of a route fordelivery of the cargo. According to some embodiments, AI logic and/oralgorithms may be utilized to suggest multiple routes “A”, “B”, and/or“C” to the driver. As depicted in FIG. 5C, each of the available and/orsuggested routes may be output and/or depicted to the driver via therouting map 520-12. In some embodiments, characteristics and/orattributes of the payload, cargo, and/or materials/contents may beutilized to compute the routes “A”, “B”, and/or “C” and the driver/usermay review and/or select one or more of the routes “A”, “B”, and/or “C”by activation of a respective one of the suggested route button 520-13,the minimize theft button 520-14, and/or the minimize damage button520-15.

According to some embodiments, the suggested route that may be chosenvia the suggested route button 520-13 may comprise a route calculated toachieve the lowest possible expected likelihood of theft and damage tothe cargo. Historic loss data may be analyzed to determine, for example,roads, areas, times of day, weather conditions, and/or other variablesthat have statistically been associated with higher likelihood of loss.In some embodiments, these likelihoods may be based on cargo type,vehicle type, and/or driver identity. According to some embodiments, thetype of cargo (and/or one or more attributes and/or characteristicsthereof, such as weight, temperature thresholds, packing materials used)may be utilized to identify route attributes that contribute toincreases in likelihood of theft and/or damage. It may be determined,for example, that refrigerated goods tend to experience heat damage(even when transported in reefer trailers) when a first route has beentaken for delivery, but experience less tendency for heat damage when asecond route has been taken. Similarly, it may be determined that flatscreen TVs experience a higher level of theft when transported throughcertain truck stops in a certain area, but a lower level of theft whenstops are made at standard fueling stations. According to someembodiments, route plans culminating in the lowest calculated likelihoodof theft may be suggested and/or selected via the minimize theft button520-14, and/or route plans culminating in the lowest calculatedlikelihood of damage may be suggested and/or selected via the minimizedamage button 520-15.

In some embodiments, the “Push When Ready to Begin Route” button 520-16may, upon a triggering and/or receipt of input from the user (e.g., aproperly positioned click of a mouse or other pointer with respect tothe “Push When Ready to Begin Route” button 520-16), call, initialize,and/or cause a generation of a routing application interface (not shown)and/or a fourth version (or page or instance) of the interface 520 d. Asdepicted in FIG. 5D, for example, the fourth version (or page orinstance) of the interface 520 d may comprise a “Cargo Status Check”interface (e.g., defining a fourth input and/or output mechanism) thatprovides and/or includes a status manifest 520-17, an initiate AR viewbutton 520-18, a sensor calibration button 520-19, a “refresh all”button 520-20, and/or “Push to Initiate Reporting” button 520-21 (e.g.,each of which may comprise one or more data entry mechanisms, tools,objects, and/or features). According to some embodiments, the fourthversion (or page or instance) of the interface 520 d may be provided inresponse to an automated cargo check performed at predefined timeintervals, in response to a triggering event (e.g., a temperature orother sensor threshold violation), and/or randomly. In some embodiments,the fourth version (or page or instance) of the interface 520 d may beutilized by a user, such as a freight hauler or driver, to view, update,and/or manage the status of the transported cargo. According to someembodiments, the status manifest 520-17 may comprise an indication ofthe status of one or more items of cargo and/or may include selectableoptions for scanning, viewing, locating, and/or selecting various itemsof cargo. In some embodiments, the initiate AR view button 520-18 maycause an AR interface (not shown; such as the interface 320 of FIG. 3)to be displayed to the user, via which the user may view graphicalrepresentations of the status of various cargo items. According to someembodiments, the sensor calibration button 520-19 may allow the user tocalibrate, reset, and/or manage one or more cargo sensors, e.g., toreduce false positive alarms and/or to account for local environmentalconditions (such as high humidity areas, rough roads, extreme coldareas). In some embodiments, the “refresh all” button 520-20 may allowthe user to query and/or update all available status metrics at once,e.g., as opposed to selectively reviewing and/or checking individualitems from the status manifest 520-17.

According to some embodiments, the “Push to Initiate Reporting” button520-21 may, upon a triggering and/or receipt of input from the user(e.g., a properly positioned click of a mouse or other pointer withrespect to the “Push to Initiate Reporting” button 520-21), call,initialize, and/or cause a generation of a fifth version (or page orinstance) of the interface 520 e. As depicted in FIG. 5E, for example,the fifth version (or page or instance) of the interface 520 e maycomprise a “Reporting and Recovery” interface (e.g., defining a fifthinput and/or output mechanism) that provides and/or includes lawenforcement contact data 520-22, insurance carrier contact data 520-23,a find nearest police button 520-24, an upload theft data button 520-25,a policy query button 520-26, an upload claim data button 520-27, anactivate tracking button 520-28, an auto report settings button 520-29,and/or an order replacement goods button 520-30 (e.g., each of which maycomprise one or more data entry mechanisms, tools, objects, and/orfeatures). In some embodiments, the fifth version (or page or instance)of the interface 520 e may be utilized by a driver, contractor, and/orother user to report a theft and/or loss event, e.g., that has beenautomatically identified by AI analysis.

According to some embodiments, the law enforcement contact data 520-22may comprise data identifying one or more law enforcement agencies thatmay be contacted to report a theft event. The law enforcement contactdata 520-22 may comprise, for example, links, buttons, and/or featuresto permit the user to place a phone call and/or initiate a textmessaging session with the identified agency. In some embodiments, thefind nearest police button 520-24 may be utilized to automaticallysearch for and/or identify a local police authority (e.g., in the casethat the cargo is in-transit and the driver is unfamiliar with the localpolice contact information). According to some embodiments, the uploadtheft data button 520-25 may be utilized to automatically transmit oneor more files and/or data elements descriptive of the stolen cargo tothe identified agency. The transmitted data may comprise, for example,images of the cargo, identification numbers, sensor and/or trackingdata, value information, theft event location, time, etc.

In some embodiments, the insurance carrier contact data 520-23 maycomprise data identifying an insurance carrier, bonding agent, broker,and/or other underwriting agency that may be contacted to file a claimbased on a loss event (e.g., theft and/or damage). The insurance carriercontact data 520-23 may comprise, for example, links, buttons, and/orfeatures to permit the user to place a phone call and/or initiate a textmessaging session with the identified carrier/agency. In someembodiments, the policy query button 520-26 may be utilized toautomatically access and/or evaluate insurance policy terms, such asdeductibles, loss parameters, coverage limits, etc. According to someembodiments, the upload claim data button 520-27 may be utilized toautomatically transmit one or more files and/or data elementsdescriptive of the stolen and/or damaged cargo to the identifiedcarrier/agency. The transmitted data may comprise, for example, imagesof the cargo, identification numbers, sensor and/or tracking data, valueinformation, theft and/or damage event location, time, etc.

According to some embodiments, the activate tracking button 520-28 maybe utilized to initialize an active tracking device associated withstolen cargo items. A GPS or other satellite-based communication,tracking, and/or location homing device may be activated, for example,so that the stolen goods may be tracked down. In some embodiments, theauto report settings button 520-29 may be utilized to access, set,and/or edit various parameters, contact data, and/or preferencesregarding how and/or when parties may be automatically contacted basedon triggering events. According to some embodiments, the orderreplacement goods button 520-30 may be utilized to automatically placean order for additional goods to replace any stolen and/or damagedgoods. In such a manner, for example, the delay in shipping and/orreceiving of the full complement of cargo items may be minimized byquickly initiating replacements.

While various components of the interfaces 520 a-e have been depictedwith respect to certain labels, layouts, headings, titles, and/orconfigurations, these features have been presented for reference andexample only. Other labels, layouts, headings, titles, and/orconfigurations may be implemented without deviating from the scope ofembodiments herein. Similarly, while a certain number of tabs,information screens, form fields, and/or data entry options have beenpresented, variations thereof may be practiced in accordance with someembodiments.

VI. AI Driving Analysis and Incentive Apparatus and Articles ofManufacture

Turning to FIG. 6, a block diagram of an AI device or other apparatus610 according to some embodiments is shown. In some embodiments, theapparatus 610 may be similar in configuration and/or functionality toany of the user devices 102 a-b, 202, 302, 502, the third-party device108, the servers 110, 210, 610 all of FIG. 1, FIG. 2A, FIG. 2B, FIG. 3,FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E, and/or FIG. 6 herein. Theapparatus 610 may, for example, execute, process, facilitate, and/orotherwise be associated with the method 400 of FIG. 4 herein, and/orportions thereof. In some embodiments, the apparatus 610 may comprise aprocessing device 612, a transceiver device 614, an input device 616, anoutput device 618, an interface 620, a memory device 640 (storingvarious programs and/or instructions 642 and data 644), and/or a coolingdevice 650. According to some embodiments, any or all of the components612, 614, 616, 618, 620, 640, 642, 644, 650 of the apparatus 610 may besimilar in configuration and/or functionality to any similarly namedand/or numbered components described herein. Fewer or more components612, 614, 616, 618, 620, 640, 642, 644, 650 and/or variousconfigurations of the components 612, 614, 616, 618, 620, 640, 642, 644,650 be included in the apparatus 610 without deviating from the scope ofembodiments described herein.

According to some embodiments, the processor 612 may be or include anytype, quantity, and/or configuration of processor that is or becomesknown. The processor 612 may comprise, for example, an Intel® IXP 2800network processor or an Intel® XEON™ Processor coupled with an Intel®E6501 chipset. In some embodiments, the processor 612 may comprisemultiple inter-connected processors, microprocessors, and/ormicro-engines. According to some embodiments, the processor 612 (and/orthe apparatus 610 and/or other components thereof) may be supplied powervia a power supply (not shown), such as a battery, an AlternatingCurrent (AC) source, a Direct Current (DC) source, an AC/DC adapter,solar cells, and/or an inertial generator. In the case that theapparatus 610 comprises a server, such as a blade server, necessarypower may be supplied via a standard AC outlet, power strip, surgeprotector, and/or Uninterruptible Power Supply (UPS) device.

In some embodiments, the transceiver device 614 may comprise any type orconfiguration of communication device that is or becomes known orpracticable. The transceiver device 614 may, for example, comprise aNetwork Interface Card (NIC), a telephonic device, a cellular networkdevice, a router, a hub, a modem, and/or a communications port or cable.According to some embodiments, the transceiver device 614 may also oralternatively be coupled to the processor 612. In some embodiments, thetransceiver device 614 may comprise an IR, RF, Bluetooth™, Near-FieldCommunication (NFC), and/or Wi-Fi® network device coupled to facilitatecommunications between the processor 612 and another device (not shown).

According to some embodiments, the input device 616 and/or the outputdevice 618 may be communicatively coupled to the processor 612 (e.g.,via wired and/or wireless connections and/or pathways) and they maygenerally comprise any types or configurations of input and outputcomponents and/or devices that are or become known, respectively. Theinput device 616 may comprise, for example, a keyboard that allows anoperator of the apparatus 610 to interface with the apparatus 610 (e.g.,a driver to retrieve AI cargo status data, as described herein). Theoutput device 618 may, according to some embodiments, comprise a displayscreen and/or other practicable output component and/or device. Theoutput device 618 may, for example, provide an interface (such as theinterface 620 and/or the interfaces 320, 520 a-e of FIG. 3, FIG. 5A,FIG. 5B, FIG. 5C, FIG. 5D, and/or FIG. 5E herein) via which AI theftprevention and/or recovery data or information is provided to a user(e.g., via a website and/or mobile application). According to someembodiments, the input device 616 and/or the output device 618 maycomprise and/or be embodied in a single device, such as a touch-screenmonitor or display.

The memory device 640 may comprise any appropriate information storagedevice that is or becomes known or available, including, but not limitedto, units and/or combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, and/or semiconductor memorydevices, such as RAM devices, Read Only Memory (ROM) devices, SingleData Rate Random Access Memory (SDR-RAM), Double Data Rate Random AccessMemory (DDR-RAM), and/or Programmable Read Only Memory (PROM). Thememory device 640 may, according to some embodiments, store one or moreof object detection instructions 642-1, routing instructions 642-2, lossinstructions 642-3 (e.g., detection and/or classification), interfaceinstructions 642-4, vehicle data 644-1, sensor data 644-2, contents data644-3, tracking data 644-4, and/or insurance data 644-5. In someembodiments, the object detection instructions 642-1, routinginstructions 642-2, loss instructions 642-3 (e.g., detection and/orclassification), interface instructions 642-4, vehicle data 644-1,sensor data 644-2, contents data 644-3, tracking data 644-4, and/orinsurance data 644-5 may be utilized by the processor 612 to provideoutput information via the output device 618 and/or the transceiverdevice 614.

According to some embodiments, the object detection instructions 642-1may be operable to cause the processor 612 to process vehicle data644-1, sensor data 644-2, contents data 644-3, tracking data 644-4,and/or insurance data 644-5 in accordance with embodiments as describedherein. Vehicle data 644-1, sensor data 644-2, contents data 644-3,tracking data 644-4, and/or insurance data 644-5 received via the inputdevice 616 and/or the transceiver device 618 may, for example, beanalyzed, sorted, filtered, decoded, decompressed, ranked, scored,plotted, and/or otherwise processed by the processor 612 in accordancewith the object detection instructions 642-1. In some embodiments,vehicle data 644-1, sensor data 644-2, contents data 644-3, trackingdata 644-4, and/or insurance data 644-5 may be fed by the processor 612through one or more mathematical and/or statistical formulas and/ormodels in accordance with the object detection instructions 642-1 toanalyze captured data, such as captured image data descriptive of cargoand/or materials or other contents, to identify one or more objects, asdescribed herein.

In some embodiments, the routing instructions 642-2 may be operable tocause the processor 612 to process the vehicle data 644-1, sensor data644-2, contents data 644-3, tracking data 644-4, and/or insurance data644-5 in accordance with embodiments as described herein. Vehicle data644-1, sensor data 644-2, contents data 644-3, tracking data 644-4,and/or insurance data 644-5 received via the input device 616 and/or thetransceiver device 618 may, for example, be analyzed, sorted, filtered,decoded, decompressed, ranked, scored, plotted, and/or otherwiseprocessed by the processor 612 in accordance with the routinginstructions 642-2. In some embodiments, vehicle data 644-1, sensor data644-2, contents data 644-3, tracking data 644-4, and/or insurance data644-5 may be fed by the processor 612 through one or more mathematicaland/or statistical formulas and/or models in accordance with the routinginstructions 642-2 to identify, calculate, and/or compute one or morecargo, vehicle, and/or driver-specific transportation routes to reducethe likelihood of loss based on historical loss data, as describedherein.

According to some embodiments, the loss instructions 642-3 may beoperable to cause the processor 612 to process the vehicle data 644-1,sensor data 644-2, contents data 644-3, tracking data 644-4, and/orinsurance data 644-5 in accordance with embodiments as described herein.Vehicle data 644-1, sensor data 644-2, contents data 644-3, trackingdata 644-4, and/or insurance data 644-5 received via the input device616 and/or the transceiver device 618 may, for example, be analyzed,sorted, filtered, decoded, decompressed, ranked, scored, plotted, and/orotherwise processed by the processor 612 in accordance with the lossinstructions 642-3. In some embodiments, vehicle data 644-1, sensor data644-2, contents data 644-3, tracking data 644-4, and/or insurance data644-5 may be fed by the processor 612 through one or more mathematicaland/or statistical formulas and/or models in accordance with the lossinstructions 642-3 to identify, detail, and/or report theft, damage,and/or other loss events utilizing AI logic, as described herein.

In some embodiments, the interface instructions 642-4 may be operable tocause the processor 612 to process the vehicle data 644-1, sensor data644-2, contents data 644-3, tracking data 644-4, and/or insurance data644-5 in accordance with embodiments as described herein. Vehicle data644-1, sensor data 644-2, contents data 644-3, tracking data 644-4,and/or insurance data 644-5 received via the input device 616 and/or thetransceiver device 618 may, for example, be analyzed, sorted, filtered,decoded, decompressed, ranked, scored, plotted, and/or otherwiseprocessed by the processor 612 in accordance with the interfaceinstructions 642-4. In some embodiments, vehicle data 644-1, sensor data644-2, contents data 644-3, tracking data 644-4, and/or insurance data644-5 may be fed by the processor 612 through one or more mathematicaland/or statistical formulas and/or models in accordance with theinterface instructions 642-4 to provide an interface (such as theinterfaces 320, 520 a-3 of FIG. 2, FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D,and/or FIG. 5E herein) via which input and/or output descriptive of AItheft prevention and recovery may be provided, as described herein.

According to some embodiments, the apparatus 610 may comprise thecooling device 650. According to some embodiments, the cooling device650 may be coupled (physically, thermally, and/or electrically) to theprocessor 612 and/or to the memory device 640. The cooling device 650may, for example, comprise a fan, heat sink, heat pipe, radiator, coldplate, and/or other cooling component or device or combinations thereof,configured to remove heat from portions or components of the apparatus610.

Any or all of the exemplary instructions and data types described hereinand other practicable types of data may be stored in any number, type,and/or configuration of memory devices that is or becomes known. Thememory device 640 may, for example, comprise one or more data tables orfiles, databases, table spaces, registers, and/or other storagestructures. In some embodiments, multiple databases and/or storagestructures (and/or multiple memory devices 640) may be utilized to storeinformation associated with the apparatus 610. According to someembodiments, the memory device 640 may be incorporated into and/orotherwise coupled to the apparatus 610 (e.g., as shown) or may simply beaccessible to the apparatus 610 (e.g., externally located and/orsituated).

Referring now to FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, and FIG. 7E,perspective diagrams of exemplary data storage devices 740 a-e accordingto some embodiments are shown. The data storage devices 740 a-e may, forexample, be utilized to store instructions and/or data, such as theobject detection instructions 642-1, routing instructions 642-2, lossinstructions 642-3 (e.g., detection and/or classification), interfaceinstructions 642-4, vehicle data 644-1, sensor data 644-2, contents data644-3, tracking data 644-4, and/or insurance data 644-5, each of whichis presented in reference to FIG. 6 herein. In some embodiments,instructions stored on the data storage devices 740 a-e may, whenexecuted by a processor, cause the implementation of and/or facilitatethe method 400 of FIG. 4 herein, and/or portions thereof.

According to some embodiments, the first data storage device 740 a maycomprise one or more various types of internal and/or external harddrives. The first data storage device 740 a may, for example, comprise adata storage medium 746 that is read, interrogated, and/or otherwisecommunicatively coupled to and/or via a disk reading device 848. In someembodiments, the first data storage device 740 a and/or the data storagemedium 746 may be configured to store information utilizing one or moremagnetic, inductive, and/or optical means (e.g., magnetic, inductive,and/or optical-encoding). The data storage medium 746, depicted as afirst data storage medium 746 a for example (e.g., breakoutcross-section “A”), may comprise one or more of a polymer layer 746 a-1,a magnetic data storage layer 746 a-2, a non-magnetic layer 746 a-3, amagnetic base layer 746 a-4, a contact layer 746 a-5, and/or a substratelayer 746 a-6. According to some embodiments, a magnetic read head 748 amay be coupled and/or disposed to read data from the magnetic datastorage layer 746 a-2.

In some embodiments, the data storage medium 746, depicted as a seconddata storage medium 746 b for example (e.g., breakout cross-section“B”), may comprise a plurality of data points 746 b-2 disposed with thesecond data storage medium 746 b. The data points 746 b-2 may, in someembodiments, be read and/or otherwise interfaced with via alaser-enabled read head 748 b disposed and/or coupled to direct a laserbeam through the second data storage medium 746 b.

In some embodiments, the second data storage device 740 b may comprise aCD, CD-ROM, DVD, Blu-Ray™ Disc, and/or other type of optically-encodeddisk and/or other storage medium that is or becomes know or practicable.In some embodiments, the third data storage device 740 c may comprise aUSB keyfob, dongle, and/or other type of flash memory data storagedevice that is or becomes know or practicable. In some embodiments, thefourth data storage device 740 d may comprise RAM of any type, quantity,and/or configuration that is or becomes practicable and/or desirable. Insome embodiments, the fourth data storage device 740 d may comprise anoff-chip cache, such as a Level 2 (L2) cache memory device. According tosome embodiments, the fifth data storage device 740 e may comprise anon-chip memory device, such as a Level 1 (L1) cache memory device.

The data storage devices 740 a-e may generally store programinstructions, code, and/or modules that, when executed by a processingdevice cause a particular machine to function in accordance with one ormore embodiments described herein. The data storage devices 740 a-edepicted in FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, and FIG. 7E arerepresentative of a class and/or subset of computer-readable media thatare defined herein as “computer-readable memory” (e.g., non-transitorymemory devices as opposed to transmission devices or media).

Throughout the description herein and unless otherwise specified, thefollowing terms may include and/or encompass the example meaningsprovided. These terms and illustrative example meanings are provided toclarify the language selected to describe embodiments both in thespecification and in the appended claims, and accordingly, are notintended to be generally limiting. While not generally limiting andwhile not limiting for all described embodiments, in some embodiments,the terms are specifically limited to the example definitions and/orexamples provided. Other terms are defined throughout the presentdescription.

Some embodiments described herein are associated with a “user device” ora “network device”. As used herein, the terms “user device” and “networkdevice” may be used interchangeably and may generally refer to anydevice that can communicate via a network. Examples of user or networkdevices include a PC, a workstation, a server, a printer, a scanner, afacsimile machine, a copier, a Personal Digital Assistant (PDA), astorage device (e.g., a disk drive), a hub, a router, a switch, and amodem, a video game console, or a wireless phone. User and networkdevices may comprise one or more communication or network components. Asused herein, a “user” may generally refer to any individual and/orentity that operates a user device. Users may comprise, for example,customers, consumers, product underwriters, product distributors,customer service representatives, agents, brokers, etc.

As used herein, the term “network component” may refer to a user ornetwork device, or a component, piece, portion, or combination of useror network devices. Examples of network components may include a StaticRandom Access Memory (SRAM) device or module, a network processor, and anetwork communication path, connection, port, or cable.

In addition, some embodiments are associated with a “network” or a“communication network”. As used herein, the terms “network” and“communication network” may be used interchangeably and may refer to anyobject, entity, component, device, and/or any combination thereof thatpermits, facilitates, and/or otherwise contributes to or is associatedwith the transmission of messages, packets, signals, and/or other formsof information between and/or within one or more network devices.Networks may be or include a plurality of interconnected networkdevices. In some embodiments, networks may be hard-wired, wireless,virtual, neural, and/or any other configuration of type that is orbecomes known. Communication networks may include, for example, one ormore networks configured to operate in accordance with the Fast EthernetLAN transmission standard 802.3-2002® published by the Institute ofElectrical and Electronics Engineers (IEEE). In some embodiments, anetwork may include one or more wired and/or wireless networks operatedin accordance with any communication standard or protocol that is orbecomes known or practicable.

As used herein, the terms “information” and “data” may be usedinterchangeably and may refer to any data, text, voice, video, image,message, bit, packet, pulse, tone, waveform, and/or other type orconfiguration of signal and/or information. Information may compriseinformation packets transmitted, for example, in accordance with theInternet Protocol Version 6 (IPv6) standard as defined by “InternetProtocol Version 6 (IPv6) Specification” RFC 1883, published by theInternet Engineering Task Force (IETF), Network Working Group, S.Deering et al. (December 1995). Information may, according to someembodiments, be compressed, encoded, encrypted, and/or otherwisepackaged or manipulated in accordance with any method that is or becomesknown or practicable.

In addition, some embodiments described herein are associated with an“indication”. As used herein, the term “indication” may be used to referto any indicia and/or other information indicative of or associated witha subject, item, entity, and/or other object and/or idea. As usedherein, the phrases “information indicative of” and “indicia” may beused to refer to any information that represents, describes, and/or isotherwise associated with a related entity, subject, or object. Indiciaof information may include, for example, a code, a reference, a link, asignal, an identifier, and/or any combination thereof and/or any otherinformative representation associated with the information. In someembodiments, indicia of information (or indicative of the information)may be or include the information itself and/or any portion or componentof the information. In some embodiments, an indication may include arequest, a solicitation, a broadcast, and/or any other form ofinformation gathering and/or dissemination.

Numerous embodiments are described in this patent application, and arepresented for illustrative purposes only. The described embodiments arenot, and are not intended to be, limiting in any sense. The presentlydisclosed invention(s) are widely applicable to numerous embodiments, asis readily apparent from the disclosure. One of ordinary skill in theart will recognize that the disclosed invention(s) may be practiced withvarious modifications and alterations, such as structural, logical,software, and electrical modifications. Although particular features ofthe disclosed invention(s) may be described with reference to one ormore particular embodiments and/or drawings, it should be understoodthat such features are not limited to usage in the one or moreparticular embodiments or drawings with reference to which they aredescribed, unless expressly specified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. On the contrary, such devices need only transmit to eachother as necessary or desirable, and may actually refrain fromexchanging data most of the time. For example, a machine incommunication with another machine via the Internet may not transmitdata to the other machine for weeks at a time. In addition, devices thatare in communication with each other may communicate directly orindirectly through one or more intermediaries.

A description of an embodiment with several components or features doesnot imply that all or even any of such components and/or features arerequired. On the contrary, a variety of optional components aredescribed to illustrate the wide variety of possible embodiments of thepresent invention(s). Unless otherwise specified explicitly, nocomponent and/or feature is essential or required.

Further, although process steps, algorithms or the like may be describedin a sequential order, such processes may be configured to work indifferent orders. In other words, any sequence or order of steps thatmay be explicitly described does not necessarily indicate a requirementthat the steps be performed in that order. The steps of processesdescribed herein may be performed in any order practical. Further, somesteps may be performed simultaneously despite being described or impliedas occurring non-simultaneously (e.g., because one step is describedafter the other step). Moreover, the illustration of a process by itsdepiction in a drawing does not imply that the illustrated process isexclusive of other variations and modifications thereto, does not implythat the illustrated process or any of its steps are necessary to theinvention, and does not imply that the illustrated process is preferred.

“Determining” something can be performed in a variety of manners andtherefore the term “determining” (and like terms) includes calculating,computing, deriving, looking up (e.g., in a table, database or datastructure), ascertaining and the like. The term “computing” as utilizedherein may generally refer to any number, sequence, and/or type ofelectronic processing activities performed by an electronic device, suchas, but not limited to looking up (e.g., accessing a lookup table orarray), calculating (e.g., utilizing multiple numeric values inaccordance with a mathematic formula), deriving, and/or defining.

It will be readily apparent that the various methods and algorithmsdescribed herein may be implemented by, e.g., appropriately and/orspecially-programmed computers and/or computing devices. Typically aprocessor (e.g., one or more microprocessors) will receive instructionsfrom a memory or like device, and execute those instructions, therebyperforming one or more processes defined by those instructions. Further,programs that implement such methods and algorithms may be stored andtransmitted using a variety of media (e.g., computer readable media) ina number of manners. In some embodiments, hard-wired circuitry or customhardware may be used in place of, or in combination with, softwareinstructions for implementation of the processes of various embodiments.Thus, embodiments are not limited to any specific combination ofhardware and software.

A “processor” generally means any one or more microprocessors, CPUdevices, computing devices, microcontrollers, digital signal processors,or like devices, as further described herein.

The term “computer-readable medium” refers to any medium thatparticipates in providing data (e.g., instructions or other information)that may be read by a computer, a processor or a like device. Such amedium may take many forms, including but not limited to, non-volatilemedia, volatile media, and transmission media. Non-volatile mediainclude, for example, optical or magnetic disks and other persistentmemory. Volatile media include DRAM, which typically constitutes themain memory. Transmission media include coaxial cables, copper wire andfiber optics, including the wires that comprise a system bus coupled tothe processor. Transmission media may include or convey acoustic waves,light waves and electromagnetic emissions, such as those generatedduring RF and IR data communications. Common forms of computer-readablemedia include, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any othermemory chip or cartridge, a carrier wave, or any other medium from whicha computer can read.

The term “computer-readable memory” may generally refer to a subsetand/or class of computer-readable medium that does not includetransmission media, such as waveforms, carrier waves, electromagneticemissions, etc. Computer-readable memory may typically include physicalmedia upon which data (e.g., instructions or other information) arestored, such as optical or magnetic disks and other persistent memory,DRAM, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD, any other optical medium, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip orcartridge, computer hard drives, backup tapes, Universal Serial Bus(USB) memory devices, and the like.

Various forms of computer readable media may be involved in carryingdata, including sequences of instructions, to a processor. For example,sequences of instruction (i) may be delivered from RAM to a processor,(ii) may be carried over a wireless transmission medium, and/or (iii)may be formatted according to numerous formats, standards or protocols,such as Bluetooth™, TDMA, CDMA, 3G.

Where databases are described, it will be understood by one of ordinaryskill in the art that (i) alternative database structures to thosedescribed may be readily employed, and (ii) other memory structuresbesides databases may be readily employed. Any illustrations ordescriptions of any sample databases presented herein are illustrativearrangements for stored representations of information. Any number ofother arrangements may be employed besides those suggested by, e.g.,tables illustrated in drawings or elsewhere. Similarly, any illustratedentries of the databases represent exemplary information only; one ofordinary skill in the art will understand that the number and content ofthe entries can be different from those described herein. Further,despite any depiction of the databases as tables, other formats(including relational databases, object-based models and/or distributeddatabases) could be used to store and manipulate the data typesdescribed herein. Likewise, object methods or behaviors of a databasecan be used to implement various processes, such as the describedherein. In addition, the databases may, in a known manner, be storedlocally or remotely from a device that accesses data in such a database.

The present invention can be configured to work in a network environmentincluding a computer that is in communication, via a communicationsnetwork, with one or more devices. The computer may communicate with thedevices directly or indirectly, via a wired or wireless medium, such asthe Internet, LAN, WAN or Ethernet, Token Ring, or via any appropriatecommunications means or combination of communications means. Each of thedevices may comprise computers, such as those based on the Intel®Pentium® or Centrino™ processor, that are adapted to communicate withthe computer. Any number and type of machines may be in communicationwith the computer.

The present disclosure provides, to one of ordinary skill in the art, anenabling description of several embodiments and/or inventions. Some ofthese embodiments and/or inventions may not be claimed in the presentapplication, but may nevertheless be claimed in one or more continuingapplications that claim the benefit of priority of the presentapplication. Applicant intends to file additional applications to pursuepatents for subject matter that has been disclosed and enabled but notclaimed in the present application.

It will be understood that various modifications can be made to theembodiments of the present disclosure herein without departing from thescope thereof. Therefore, the above description should not be construedas limiting the disclosure, but merely as embodiments thereof. Thoseskilled in the art will envision other modifications within the scope ofthe invention as defined by the claims appended hereto.

What is claimed is:
 1. A cargo management system, comprising: anelectronic transceiver device; an electronic processing device incommunication with the electronic transceiver device; and anon-transitory memory device storing (i) Artificial Intelligence (AI)image analysis logic and (ii) operating instructions that when executedby the electronic processing device, result in: receiving, via theelectronic transceiver device and from a first remote mobile electronicdevice, data descriptive of a plurality of items comprising an instanceof cargo; receiving, via the electronic transceiver device and from thefirst remote mobile electronic device, at least one first imagedescriptive of the instance of cargo at a first point in time;computing, by an execution of the AI image analysis logic by theelectronic processing device, a correlation between one or more portionsof the at least one first image and one or more respective items fromthe plurality of items comprising the instance of cargo; receiving, viathe electronic transceiver device and from a second remote mobileelectronic device, at least one second image descriptive of the instanceof cargo at a second point in time; identifying, by an execution of theAI image analysis logic by the electronic processing device, adifference between the images, comprising at least one portion of the atleast one second image that differs from a corresponding portion of theat least one first image; identifying, based on the correlation betweenthe one or more portions of the at least one first image and the one ormore respective items from the plurality of items comprising theinstance of cargo, a first item from the plurality of items comprisingthe instance of cargo that corresponds to the identified differencebetween the images; and generating an alert including information thatidentifies the first item from the plurality of items comprising theinstance of cargo.
 2. The cargo management system of claim 1, whereinthe operating instructions, when executed by the electronic processingdevice, further result in: categorizing, by an execution of the AI imageanalysis logic by the electronic processing device, the differencebetween the images as one or more of a plurality of predefined types ofdifferences.
 3. The cargo management system of claim 2, wherein thegenerating of the alert further comprises: generating an alert includingthe categorized one or more of the plurality of predefined types ofdifferences.
 4. The cargo management system of claim 2, wherein theplurality of predefined types of differences comprises: (i) a movementof the first item; (ii) a change in a appearance of the first item; and(iii) a disappearance of the first item.
 5. The cargo management systemof claim 1, wherein the computing of the correlation between the one ormore portions of the at least one first image and the one or morerespective items from the plurality of items comprising the instance ofcargo, comprises: identifying, from the at least one first image, atleast one of: (i) a seal number; (ii) a serial number; (iii) a shape;(iv) a color; and (v) a dimension.
 6. The cargo management system ofclaim 1, wherein the data descriptive of the plurality of itemscomprises data descriptive of at least one of: (i) a quantity; (ii) atype; (iii) a position; (iv) a location; (v) an identifier; (vi) acharacteristic parameter; (vii) a monetary value; (viii) an originlocation; (ix) a destination location; (x) a manufacturer; and (xi) amodel.
 7. The cargo management system of claim 1, wherein the firstremote mobile electronic device and the second remote mobile electronicdevice comprise the same device.
 8. The cargo management system of claim1, wherein the first remote mobile electronic device is associated witha loader of the cargo and the first point in time comprises a time ofloading the cargo into a cargo container, and wherein the second remotemobile electronic device is associated with a transporter of the cargoand the second point in time comprises a time of transporting the cargovia the cargo container.
 9. The cargo management system of claim 8,wherein the operating instructions, when executed by the electronicprocessing device, further result in: transmitting, via the electronictransceiver device and to the first remote mobile electronic device,identifying information descriptive of the transporter.
 10. The cargomanagement system of claim 9, wherein the operating instructions, whenexecuted by the electronic processing device, further result in:receiving, via the electronic transceiver device and from the firstremote mobile electronic device, an indication that the identity of thetransporter has been verified based on the identifying informationdescriptive of the transporter.
 11. The cargo management system of claim10, wherein the operating instructions, when executed by the electronicprocessing device, further result in: transmitting, via the electronictransceiver device and to a starter device of a transport vehiclecoupled to the instance of cargo, and in response to the receiving ofthe indication that the identity of the transporter has been verifiedbased on the identifying information descriptive of the transporter, acommand that authorizes movement of the transport vehicle.
 12. The cargomanagement system of claim 8, wherein the operating instructions, whenexecuted by the electronic processing device, further result in:transmitting, via the electronic transceiver device and to the secondremote mobile electronic device, identifying information descriptive ofthe loader.
 13. The cargo management system of claim 12, wherein theoperating instructions, when executed by the electronic processingdevice, further result in: receiving, via the electronic transceiverdevice and from the second remote mobile electronic device, anindication that the identity of the loader has been verified based onthe identifying information descriptive of the loader.
 14. The cargomanagement system of claim 13, wherein the operating instructions, whenexecuted by the electronic processing device, further result in:transmitting, via the electronic transceiver device and to a starterdevice of a transport vehicle coupled to the instance of cargo, and inresponse to the receiving of the indication that the identity of theloader has been verified based on the identifying informationdescriptive of the loader, a command that authorizes movement of thetransport vehicle.
 15. The cargo management system of claim 1, whereinthe non-transitory memory device further stores (iii) risk-basednavigational routing rules, wherein the data descriptive of theplurality of items comprising the instance of cargo comprises dataidentifying at least one physical characteristic of the instance ofcargo, and wherein the operating instructions, when executed by theelectronic processing device, further result in: computing, by anexecution of the risk-based navigational routing rules by the electronicprocessing device, and based at least in part on the at least onephysical characteristic of the instance of cargo, a risk-basednavigational route for transporting the instance of cargo from a firstlocation to a second location; and transmitting, via the electronictransceiver device and to the second remote mobile electronic device,graphical information descriptive of the risk-based navigational route.